Top AI Jobs in Singapore

Lead software development for AI-driven banking solutions, ensuring high-performance, scalable code and integration with AI algorithms across financial applications.

As a Data Scientist in AI Fintech, you will build machine learning models for fraud detection, analyze data, and ensure compliance with regulations.

Ship real open-source integrations and MCP servers to bring SearchApi to agent and automation frameworks. Create PRs, example apps, docs, and integration guides. Stay current with the agent ecosystem, pick high-leverage targets, and work independently to deliver production-ready integrations.

As a Senior AI Engineer, you will utilize AI engineering and delivery expertise to drive transformative projects for clients while building relationships and expanding your knowledge.

The AI Engineer will solve complex problems using AI engineering expertise, maintain client relationships, and continuously improve skills in AI and data.

The Lead AI Engineer will solve complex AI challenges, mentor a team, and develop client relationships while delivering technical thought leadership.

Build and operate DV's model layer: fine-tune and distill open-weight models, run on-prem inference infrastructure on Kubernetes, implement a model gateway routing across providers, evaluate model quality/cost/latency, manage GPU provisioning and utilization, and define tooling for model selection and production regression testing.

Lead product planning, requirements analysis, and solution design for OKX Wallet. Produce PRDs, prototypes, and product documentation; collaborate with engineering, design, operations, and data teams; evaluate technical feasibility and trade-offs; drive 0-to-1 and iterative product launches; monitor product performance and own user experience and business outcomes.

Lead enterprise data and AI architecture strategy, ensuring alignment with business goals and guiding solution designs while managing cross-domain initiatives and compliance requirements.

Design, build, operate, and maintain hybrid AI platforms. Implement automation-first platform engineering, ensure reliability/scalability/observability, collaborate with architects and infra teams, and troubleshoot production issues.

Lead QA for AI-powered SaaS features: design and execute functional, API, integration, regression, exploratory, and end-to-end tests. Validate AI outputs, investigate root causes across frontend/backend/APIs/databases, build automation, improve CI/CD quality gates, and collaborate with engineers, product, and security teams to raise release confidence.

As an AI Engineer at SimplifyNext, you will develop machine learning models, integrate AI solutions, perform data engineering tasks, and collaborate with SMEs to deliver impactful AI-driven solutions.

As an AI Engineer, you will build and deploy AI solutions, work with data preparation, integrate models into applications, and collaborate with teams to solve business problems using AI.

Develop and validate computer vision models and workflows for soil photography and XCT/CT laboratory imaging. Tasks include image classification, detection, segmentation, sample quality assessment, dataset preparation/annotation, integration with lab/LIMS and geotechnical data, and documentation of methods and validation against expert ground truth.

Design, build and maintain scalable ETL/ELT data pipelines and integrations on Microsoft Fabric and Databricks. Prepare and model data for AI/ML and analytics, ensure data quality, lineage, security and governance, develop connectors/APIs, support CI/CD deployments, monitor pipelines, and document handovers while collaborating with stakeholders and AI/ML engineers.

Design and govern secure, scalable AI architectures for APAC, defining standards, cloud and hybrid infrastructure, MLOps/LLMOps pipelines, model selection (RAG/fine-tuning), data governance, and security controls while providing technical leadership for an AI-enabled Marine Ground Digital Twin Platform.

Build, deploy and support AI/ML and LLM-powered applications and agentic workflows. Rapidly prototype, convert to production-ready services, implement RAG and vector DB integrations, apply MLOps (versioning, monitoring, retraining), develop APIs/microservices for enterprise integration, and monitor/model performance. Collaborate with AI architects and data engineering, document solutions, and support enablement across APAC. 3-year fixed-term role based in Singapore (SG/PR only).

Work with stakeholders to identify and prioritize AI, automation and analytics use cases for a marine digital twin platform. Gather and document requirements, build low-code solutions (Power Platform), support prototypes, enable APAC adoption through training and documentation, and produce dashboards and reports to track impact.

Develop and maintain scalable backend services and APIs, collaborate with frontend developers, and lead new features and infrastructure improvements.

Design, build, and deploy end-to-end agentic AI and LLM applications for enterprise customers. Responsibilities include model development (RAG, fine-tuning), production ML pipelines, Kubernetes/Helm deployments (cloud, on-prem, air-gapped), LLMOps and monitoring, customer-facing technical delivery, troubleshooting, workshops, and ensuring secure, production-grade AI with guardrails and performance sizing.

Work with the R&D innovation team to design, implement, and optimize computer vision and perception algorithms. Develop and test object detection, tracking, and recognition models, conduct literature reviews and benchmarking, experiment with novel approaches, train and evaluate models on large datasets, and collaborate to integrate vision algorithms into production embedded systems.

Work with R&D to design, develop, and maintain AI applications and RPA solutions. Build AI/software applications and APIs, identify automation opportunities, integrate RPA bots with APIs, databases and third-party systems, and perform testing, debugging, and performance optimization.

As an AI Specialist Solution Architect, you will architect AI systems, support technical sales, engage stakeholders, and operationalize AI while advocating for Red Hat products.

Support AI Capability & Adoption through structured prompt experimentation, custom GPT testing, lightweight workflow prototyping, documentation of exemplars, and coaching/enablement to scale responsible AI usage across the firm.

Support AI capability adoption via structured prompt experimentation, custom GPT testing, workflow prototyping, documentation of prompt libraries and exemplars, and coaching/enablement sessions to scale responsible AI use across the firm.